recipe bioconductor-sigspack

Mutational Signature Estimation for Single Samples






Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues.

package bioconductor-sigspack

(downloads) docker_bioconductor-sigspack



depends bioconductor-biobase:


depends bioconductor-biostrings:


depends bioconductor-bsgenome:


depends bioconductor-genomeinfodb:


depends bioconductor-genomicranges:


depends bioconductor-rtracklayer:


depends bioconductor-summarizedexperiment:


depends bioconductor-variantannotation:


depends r-base:


depends r-quadprog:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-sigspack

and update with::

   mamba update bioconductor-sigspack

To create a new environment, run:

mamba create --name myenvname bioconductor-sigspack

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-sigspack/tags`_ for valid values for ``<tag>``)

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